Accurate Base Station Placement in 4G LTE Networks Using Multiobjective Genetic Algorithm Optimization

Algorithm 1

The adopted NSGA-II pseudocode.

Input: eNodeB placement model parameters

Input: GA control operators

Input: I (population size)

Output: O (Pareto front approximation)

Steps

i:  Define fitness functions

ii:  Create an initial random population,

iii:  Compute fitness values of each chromosome in

iv:  Rank the individuals in the population using a fast nondominated sort

v:  Compute the crowding distance of each solution

vi:  While the maximum iteration number is not reached yet, do

vii: Choose parents from through binary tournament selection with crowding distance

viii:  Employ the GA operators (crossover and mutation) to create a set of new solutions,

ix: Evaluate fitness values of solutions in

x:  Merge I ← [I, ]

xi:  Rank each solution in using a fast nondominated sort

xii: Compute the crowding distance meant for each solution in

xiii: Change solutions in with the best solution in

xiv: End while